Auto-scaling a pool of virtual delivery agents

Abstract

Systems and methods described herein provide auto-scaling of virtual delivery agent services. The system can identify data indicating consumption of a pool of active virtual delivery agents over a plurality of previous time frames. The system can determine a usage metric for a time frame of the plurality of previous time frames based on the data indicating consumption of the pool of active virtual delivery agents. The system can control, responsive to an auto-scale setting of the pool based on the usage metric, a number of active virtual delivery agents in the pool for a future time frame that corresponds to the time frame of the plurality of previous time frames.

What is claimed is: 
     
         1 . A method of auto-scaling virtual delivery agent services, comprising:
 identifying, by one or more processors, data indicating consumption of a pool of active virtual delivery agents over a plurality of previous time frames;   determining, by the one or more processors, a usage metric for a time frame of the plurality of previous time frames based on the data indicating consumption of the pool of active virtual delivery agents; and   controlling, by the one or more processors responsive to an auto-scale setting of the pool based on the usage metric, a number of active virtual delivery agents in the pool for a future time frame that corresponds to the time frame of the plurality of previous time frames.   
     
     
         2 . The method of  claim 1 , wherein the data indicating the consumption includes a time series of requests to access services provided via the pool over the plurality of previous time frames. 
     
     
         3 . The method of  claim 1 , comprising:
 generating a fitting curve based on the data indicating consumption of the pool of active virtual delivery agents; and   determining the usage metric based on the fitting curve, wherein the usage metric indicates a number of requests for services provided via virtual delivery agents predicted to occur in the future time frame and prior to receipt of the requests for the services provided via the virtual delivery agents.   
     
     
         4 . The method of  claim 1 , comprising:
 filtering the data indicating consumption of the pool of active virtual delivery agents by removing time frames corresponding to weekends and holidays;   generating a fitting curve based on the filtered data; and   determining the usage metric based on the fitting curve generated based on the filtered data.   
     
     
         5 . The method of  claim 1 , comprising:
 filtering the data by removing one or more types of time frames;   inputting the filtered data into a machine learning component to generate a weekly fitting curve and a daily fitting curve.   
     
     
         6 . The method of  claim 1 , comprising:
 establishing a safety buffer of virtual delivery agents based on the usage metric and a standard deviation of the usage metric for the plurality of previous time frames; and   establishing the auto-scale setting based on the safety buffer to control the number of active virtual delivery agents in the pool for the future time frame.   
     
     
         7 . The method of  claim 1 , comprising:
 determining, based on the auto-scale setting of the pool, to increase the number of active virtual delivery agents in the pool for the future time frame; and   pre-launching, responsive to the determination, one or more virtual delivery agents in accordance with the auto-scale setting.   
     
     
         8 . The method of  claim 1 , comprising:
 determining, for the auto-scale setting, a safety buffer of virtual delivery agents based on the usage metric and a standard deviation of the usage metric for the plurality of previous time frames;   detecting a request density for a current time frame;   determining, based on the request density and the safety buffer, to initiate a second safety buffer for the future time frame; and   initiating, responsive to the determination, the second safety buffer to increase the number of active virtual delivery agents in the pool during the future time frame.   
     
     
         9 . The method of  claim 1 , comprising:
 generating, responsive to the auto-scale setting of the pool, a pre-launch token for the future time frame; and   providing the pre-launch token to a pool manager service to cause the pool manager service to launch a virtual delivery agent for the pre-launch token prior to receiving a session request from a client device.   
     
     
         10 . The method of  claim 1 , comprising:
 detecting a request density associated with a client device or a group of client devices associated with an entity; and   determining, based on the request density and the usage metric for the time frame, to disconnect the client device or the group of client devices to maintain a predetermined number of active virtual delivery agents in the pool for the future time frame.   
     
     
         11 . A system to auto-scale virtual delivery agent services, comprising:
 a device comprising one or more processors configured to:   identify data indicating consumption of a pool of active virtual delivery agents over a plurality of previous time frames;   determine a usage metric for a time frame of the plurality of previous time frames based on the data indicating consumption of the pool of active virtual delivery agents; and   control, responsive to an auto-scale setting of the pool based on the usage metric, a number of active virtual delivery agents in the pool for a future time frame that corresponds to the time frame of the plurality of previous time frames.   
     
     
         12 . The system of  claim 11 , wherein the data indicating the consumption includes a time series of requests to access services provided via the pool over the plurality of previous time frames. 
     
     
         13 . The system of  claim 11 , wherein the device is configured to:
 generate a fitting curve based on the data indicating consumption of the pool of active virtual delivery agents; and   determine the usage metric based on the fitting curve, wherein the usage metric indicates a number of requests for services provided via virtual delivery agents predicted to occur in the future time frame and prior to receipt of the requests for the services provided via the virtual delivery agents.   
     
     
         14 . The system of  claim 11 , wherein the device is configured to:
 filter the data indicating consumption of the pool of active virtual delivery agents by removing time frames corresponding to weekends and holidays;   generate a fitting curve based on the filtered data; and   determine the usage metric based on the fitting curve generated based on the filtered data.   
     
     
         15 . The system of  claim 11 , wherein the device is configured to:
 filter the data by removing one or more types of time frames;   input the filtered data into a machine learning component to generate a weekly fitting curve and a daily fitting curve.   
     
     
         16 . The system of  claim 11 , wherein the device is configured to:
 establish a safety buffer of virtual delivery agents based on the usage metric and a standard deviation of the usage metric for the plurality of previous time frames; and   establish the auto-scale setting based on the safety buffer to control the number of active virtual delivery agents in the pool for the future time frame.   
     
     
         17 . The system of  claim 11 , wherein the device is configured to:
 determine, based on the auto-scale setting of the pool, to increase the number of active virtual delivery agents in the pool for the future time frame; and   pre-launch, responsive to the determination, one or more virtual delivery agents in accordance with the auto-scale setting.   
     
     
         18 . The system of  claim 11 , wherein the device is configured to:
 determine, for the auto-scale setting, a safety buffer of virtual delivery agents based on the usage metric and a standard deviation of the usage metric for the plurality of previous time frames;   detect a request density for a current time frame;   determine, based on the request density and the safety buffer, to initiate a second safety buffer for the future time frame; and   initiate, responsive to the determination, the second safety buffer to increase the number of active virtual delivery agents in the pool during the future time frame.   
     
     
         19 . The system of  claim 11 , wherein the device is configured to:
 generate, responsive to the auto-scale setting of the pool, a pre-launch token for the future time frame; and   provide the pre-launch token to a pool manager service to cause the pool manager service to launch a virtual delivery agent for the pre-launch token prior to receiving a session request from a client device.   
     
     
         20 . The system of  claim 11 , wherein the device is configured to:
 detect a request density associated with a client device or a group of client devices associated with an entity; and   determine, based on the request density and the usage metric for the time frame, to disconnect the client device or the group of client devices to maintain a predetermined number of active virtual delivery agents in the pool for the future time frame.